sequence_encoders.py 文件源码

def __init__(self, rnn_class=LSTM, encoder_dims=50, bidirectional=True, dropout_rate=0.5, **rnn_kwargs):
        """Creates an RNN model with attention. The attention mechanism is implemented as described
        in https://www.cs.cmu.edu/~hovy/papers/16HLT-hierarchical-attention-networks.pdf, but without
        sentence level attention.

        Args:
            rnn_class: The type of RNN to use. (Default Value = LSTM)
            encoder_dims: The number of hidden units of RNN. (Default Value: 50)
            bidirectional: Whether to use bidirectional encoding. (Default Value = True)
            **rnn_kwargs: Additional args for building the RNN.
        """
        super(AttentionRNN, self).__init__(dropout_rate)
        self.rnn_class = rnn_class
        self.encoder_dims = encoder_dims
        self.bidirectional = bidirectional
        self.rnn_kwargs = rnn_kwargs